Hi Stanislaw.
Thanks for your mail.
Such discussion should go to the scikit-learn mailing list (I just
forwarded).
We had a similar discussion about SOMs recently, and the outcome was
that we want algorithms that perform well in practice.
So if you have a machine learning problem where GNG has benefits over
standard machine learning approaches, there is definitely
interest.
It would be great if you could provide an example comparing GNG to other
approaches, particularly those already implemented in scikit-learn.

Hth,
Andy

ps: sorry for sending this out twice stanislaw, forgot the cc to the mailing 
list.

On 11/16/2013 12:54 PM, Stanislaw Jastrzebski wrote:
> Dear Sir,
>
> I wasn't sure to whom I should write this email, I am sorry if I am
> bothering you without reason and please let me know where I should
> forward this question.
>
> To be concise - I am a student from Jagiellonian University working in
> machine learning. I have developed code for Growing Neural Gas (quite
> unique because of its performance). I am currently vice-president of
> Student Robotics Association at our university and I am looking for
> project for students willing to learn more about machine learning :)
> Implementing this for scikit-learn would be a great project.
>
> I would like to ask you if there is a place for GNG in scikit-learn,
> and if the community would be interested in such thing? Code is now
> rather horrible (mainly developed as package for R in C++) - code was
> used in research (resulting in publication) and is functional (can be
> found here https://github.com/kudkudak/Growing-Neural-Gas).
>
> Thank you very much for your time,
> Stanislaw Jastrzebski




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